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About: Generalized Boosted Regression Models Changes:Fetched by rcranrobot on 20130401 00:00:05.019963

About: Variable selection using random forests Changes:Fetched by rcranrobot on 20120201 00:00:12.245883

About: ALGLIB is an open source numerical analysis library distributed under GPL 2+. It implements both general numerical algorithms and machine learning algorithms. ALGLIB can be used from C#, C++, FreePascal, VBA and other languages. It is the only numerical analysis library which uses automatic translation to generate source code written in different programming languages with 100% identical functionality. Changes:

About: BenchMarking Via Weka is a clientserver architecture that supports interoperability between different machine learning systems. Machine learning systems need to provide mechanisms for processing [...] Changes:Initial Announcement on mloss.org.

About: THIS VERSION DISCONTINUED, see "http://mloss.org/software/view/424/". This library provides ways of computing generalised 2ndorder Stirling numbers for PitmanYor and Dirichlet processes. Included is a tester and parameter optimiser. This accompanies Buntine and Hutter's article: http://arxiv.org/abs/1007.0296 Changes:See the alternative MLOSS entry "libstb". Updated to 1.4!

About: The Ktree is a scalable approach to clustering inspired by the B+tree and kmeans algorithms. Changes:Release of Ktree implementation in Python. This is targeted at a research and rapid prototyping audience.

About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. Changes:

About: A generalized version of spectral clustering using the graph pLaplacian. Changes:

About: This package contains an implementation of the Infinite Kernel Learning (IKL) algorithm and the SimpleMKL algorithm. This is realized by building on CoinIpopt3.3.5 and Libsvm. Changes:Initial Announcement on mloss.org.

About: LaRank is an online solver for multiclass Support Vector Machines. Changes:Initial Announcement on mloss.org.
